Practical evaluation of intelligent algorithms in ESG management of manufacturing enterprises.

ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limitations such as low efficiency and lack of objectivity. To improve the efficiency and accuracy of ESG evaluation and promote the optimization of ESG performance in manufacturing enterprises, this article combined data mining and analytic hierarchy process (AHP) to conduct effective research on ESG management practice evaluation in manufacturing enterprises. This article adopted the best priority search strategy to collect and process enterprise ESG data. By using AHP to construct hierarchical and segmented objectives for target problems, a performance evaluation index system for management practices was built based on the evaluation objectives and hierarchical priority order. Finally, based on the performance evaluation of ESG management practices, the K-nearest Neighbor algorithm was applied to analyze historical data of key indicators. According to the weights, various key indicators were re-integrated, achieving practical evaluation and decision support for enterprise ESG management. To verify the effectiveness of data mining and AHP, this article took Z enterprise as the research object and conducted empirical analysis on it. The results showed that in terms of evaluation accuracy, the method proposed in this article achieved the highest evaluation accuracy of 92.51%, 91.16%, and 91.75% in environmental, social, and governance dimension data use case evaluation, respectively. The conclusion indicated that data mining and AHP could improve the accuracy of ESG management practice evaluation in enterprises, provide reliable decision support for enterprise development, and help promote sustainable development of enterprises.

Analysing tie-break performance of professional tennis players at Grand Slam matches.

A tiebreak in tennis is one of the critical moments where players are expected to excel under mental pressure and maintain high level of performance. Despite the importance of tiebreak points, research exploring the performance of male and female players during such match phrase remains limited. This study aimed to investigate i) the overall tiebreak performance of male and female players in relation to the outcome, ii) to examine their point-level performance by considering different contextual variables. A total of 535 tiebreaks comprising 6380 points from the 2016-2021 US Open men's and women's singles matches were collected. The difference in match performance between winning and losing players within the entire tiebreak game was explored. A subsequent decision tree analysis was then used to analyse the effect of the contextual and performance variables on tiebreak point-by-point outcome. The results showed that male and female Winning players outperformed the Losing players in 1st Serve, Serve Width and Net approach performance. The analysis of point-level performance showed that Net point, Score scene, and Point server substantially impacted tennis players' tiebreak outcome. These findings provide valuable insight for coaches and players, informing tiebreak tactics tailoring and training in relevance to different match status.

Study of Deep Learning in Medical Education: Opportunities, Achievements and Future Challenges.

Introduction: In this era of progress, interest has developed regarding advancing deep learning (DL) in medicine. However, there has been reluctance to use deep learning, particularly among medical educators. The limitations of previous research were examined in this study, along with the extent to which DL can be used in medical education and its potential impact on educational quality. We were interested in discussing DL's prospects, and determining whether we could benefit from it in medical education. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was used to manage this review procedure. Six databases were searched carefully to obtain relevant studies. Our search identified 981 articles from the database based on our standards. After filtering the duplicated articles, 11 studies were included in the systematic review. Results: The results showed that DL applications attracted researchers' attention in the medical and education technology owing to their effectiveness to provide the personalized assistance and feedback. Furthermore, the majority of research concentrated on teaching medical students how to utilize DL applications in the classroom, and all of them tried to improve medical students' proficiency with DL instruments in practical applications. Deep learning components in medical learning environments have two segments-in the educational settings like speech recognition or Video content analysis for affecting students' learning, and in the medical settings, applying deep learning from diagnosis to prevention. An integration of them can work better in medical education. Conclusion: Medical education uses DL to improve the students' education. DL is a powerful instrument which has become more famous in terms of superb outcomes. Besides, using DL in medical education is likely to continue as a hotly debated area of research and a well-known classroom strategy.

GALV-KoRV-related retroviruses in diverse Australian and African rodent species.

The enigmatic origins and transmission events of the gibbon ape leukemia virus (GALV) and its close relative the koala retrovirus (KoRV) have been a source of enduring debate. Bats and rodents are each proposed as major reservoirs of interspecies transmission, with ongoing efforts to identify additional animal hosts of GALV-KoRV-related retroviruses. In this study, we identified nine rodent species as novel hosts of GALV-KoRV-related retroviruses. Included among these hosts are two African rodents, revealing the first appearance of this clade beyond the Australian and Southeast Asian region. One of these African rodents, Mastomys natalensis, carries an endogenous GALV-KoRV-related retrovirus that is fully intact and potentially still infectious. Our findings support the hypothesis that rodents are the major carriers of GALV-KoRV-related retroviruses.

An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies.

Despite decades of antibody research, it remains challenging to predict the specificity of an antibody solely based on its sequence. Two major obstacles are the lack of appropriate models and the inaccessibility of datasets for model training. In this study, we curated >5,000 influenza hemagglutinin (HA) antibodies by mining research publications and patents, which revealed many distinct sequence features between antibodies to HA head and stem domains. We then leveraged this dataset to develop a lightweight memory B cell language model (mBLM) for sequence-based antibody specificity prediction. Model explainability analysis showed that mBLM could identify key sequence features of HA stem antibodies. Additionally, by applying mBLM to HA antibodies with unknown epitopes, we discovered and experimentally validated many HA stem antibodies. Overall, this study not only advances our molecular understanding of the antibody response to the influenza virus but also provides a valuable resource for applying deep learning to antibody research.

Data mining and safety analysis of dual orexin receptor antagonists (DORAs): a real-world pharmacovigilance study based on the FAERS database.

Objective: Using the Food and Drug Administration Adverse Event Reporting System (FAERS) database, four signal detection methods were applied to mine adverse drug events (ADEs) related to use of dual orexin receptor antagonists (DORAs) to provide reference for safe clinical use. Research design and Methods: Data collected from Q3rd 2014 to Q4th 2023 were obtained from the FAERS database. According to the preferred terminology (PT) and systematic organ classification (SOC) of MedDRA v.26.0, the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to detect ADE signals. Results: A total of 11,857 DORAs-related adverse reactions were detected, reported with suvorexant, lemborexant, and daridorexant as the main suspected drugs was 8717584, and 2556, respectively. A higher proportion of females than males were reported (57.27% vs. 33.04%). The top 20 positive PT signals from three DORAs showed that "sleep paralysis" ranked first. "Brain fog" was stronger following daridorexant but was not detected for the other two drugs, and "sleep sex" and "dyssomnia" were stronger in suvorexant but not in the other two drugs. Additionally, some PTs occurred that were not included in drug instructions, such as "hangover" and "hypnagogic hallucination." Conclusion: In this study, four algorithms (ROR, PRR, BCPNN, and MGPS) were used to mine the safety signals of DORAs. We identified some potential ADE signals that can promote the rational use of DORAs and improve their safety.

Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges.

In recent years, e-commerce platforms have become popular and transformed the way people buy and sell goods. People are rapidly adopting Internet shopping due to the convenience of purchasing from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses increasingly rely on online reviews to assess and improve the quality of products. Existing literature uses natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis and opinion mining, review analysis and management, customer experience and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey also addresses open issues, providing future directions in analyzing online customer reviews.

Research on the shared function of central neurons and breast cancer based on gene expression profile data mining: The role of EMID1 protein antibody expression.

In recent years, the incidence of breast cancer has gradually increased, and the research on it has become a hot spot in the scientific community. Central neurons play an important role in breast cancer. This study aims to explore the application of gene expression profile data mining in the study of shared function between central neurons and breast cancer, and focuses on the expression of EMID1 protein antibody. The study collected biomedical images and gene expression profile data of breast cancer patients. Then, we use image processing and analysis technology to extract and analyze features of biomedical images to obtain quantitative features of breast cancer. Gene expression profile data were preprocessed and analyzed to obtain information about breast cancer related genes. Integrating and fusing biomedical images and gene expression profile data, and exploring the sharing function between central neurons and breast cancer through data mining algorithms and statistical analysis methods. The results showed that the expression of EMID1 protein was high in breast cancer tissues, and the expression pattern was similar to that of central neurons. Further functional studies have shown that EMID1 protein is involved in the regulation of proliferation and invasion of breast cancer cells. By regulating the expression level of EMID1 protein, we observed that the proliferation and invasion ability of breast cancer cells were significantly affected. The research results show that through the comprehensive analysis of biomedical images and gene expression profile data, we found the sharing function between central neurons and breast cancer. The central neuronal cell marker genes EMID1 and GREB1L may be used as key biomarkers to regulate the pathogenesis of breast cancer and affect the occurrence and development of breast cancer.

A knowledge graph algorithm enabled deep recommendation system.

Personalized learning resource recommendations may help resolve the difficulties of online education that include learning mazes and information overload. However, existing personalized learning resource recommendation algorithms have shortcomings such as low accuracy and low efficiency. This study proposes a deep recommendation system algorithm based on a knowledge graph (D-KGR) that includes four data processing units. These units are the recommendation unit (RS unit), the knowledge graph feature representation unit (KGE unit), the cross compression unit (CC unit), and the feature extraction unit (FE unit). This model integrates technologies including the knowledge graph, deep learning, neural network, and data mining. It introduces cross compression in the feature learning process of the knowledge graph and predicts user attributes. Multimodal technology is used to optimize the process of project attribute processing; text type attributes, multivalued type attributes, and other type attributes are processed separately to reconstruct the knowledge graph. A convolutional neural network algorithm is introduced in the reconstruction process to optimize the data feature qualities. Experimental analysis was conducted from two aspects of algorithm efficiency and accuracy, and the particle swarm optimization, neural network, and knowledge graph algorithms were compared. Several tests showed that the deep recommendation system algorithm had obvious advantages when the number of learning resources and users exceeded 1,000. It has the ability to integrate systems such as the particle swarm optimization iterative classification, neural network intelligent simulation, and low resource consumption. It can quickly process massive amounts of information data, reduce algorithm complexity and requires less time and had lower costs. Our algorithm also has better efficiency and accuracy.

Exploration of Therapeutic Strategies of Herbal Prescriptions for Carbuncle Treatment to Suggest Modern Approaches to Inflammatory Bowel Disease: Cluster and Network Analyses of the Book «Liu Juan Zi Gui Yi Fang¼.

Inflammatory bowel disease (IBD) treatments in East Asian traditional medicine (EATM) originate from principles for treating abscesses and carbuncles. Understanding the therapeutic principles of Liu Juan Zi Gui Yi Fang (GYF) is essential for optimizing EATM treatment strategies for IBD, but quantitative analysis is lacking. This study aims to extract quantitative information on therapeutic strategies from GYF and present the EATM conceptual framework for IBD treatment. Oral prescriptions for carbuncles were selected, and their constituent herbs and indications were standardized and tokenized for analysis. An EATM expert group classified prescriptions based on the similarity of herbs and indications. Hierarchical and k-means cluster analyses were performed based on herb similarity. The herb-indication (H-I) network for all prescriptions was constructed. Additionally, H-I subnetworks based on the expert group's classifications and the k-means clustering results were constructed and compared to identify treatment goals and the herbs used for each goal. The results showed that the treatment focused on abscess status, wound healing, and patient's recovery capacity, with 'fever' and 'deficiency' as the main indications addressed by tonifying and anti-inflammatory herbs. The therapeutic principles identified in this study can serve as a foundation for developing future herbal intervention units. Further preclinical and clinical research is needed to validate these findings.

Industry 4.0 Technologies in Maternal Health Care: Bibliometric Analysis and Research Agenda.

BACKGROUND: Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients. OBJECTIVE: This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations. METHODS: A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team). RESULTS: A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022. CONCLUSIONS: This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Uric acid is associated with type 2 diabetes: data mining approaches.

Background: Several blood biomarkers have been related to the risk of type 2 diabetes mellitus (T2D); however, their predictive value has seldom been assessed using data mining algorithms. Methods: This cohort study was conducted on 9704 participants recruited from the Mashhad Stroke and Heart Atherosclerotic disorders (MASHAD) study from 2010 to 2020. Individuals who were not between the ages of 35 and 65 were excluded. Serum levels of biochemical factors such as creatinine (Cr), high-sensitivity C reactive protein (hs-CRP), Uric acid, alanine aminotransferase (ALT), aspartate aminotransferase (AST), direct and total bilirubin (BIL.D, BIL.T), lipid profile, besides body mass index (BMI), waist circumference (WC), blood pressure, and age were evaluated through Logistic Regression (LR) and Decision Tree (DT) methods to develop a predicting model for T2D. Results: The comparison between diabetic and non-diabetic participants represented higher levels of triglyceride (TG), LDL, cholesterol, ALT, BIL.D, and Uric acid in diabetic cases (p-value < 0.05). The LR model indicated a significant association between TG, Uric acid, and hs-CRP, besides age, sex, WC, and blood pressure, hypertension and dyslipidemia history with T2D development. DT algorithm demonstrated dyslipidemia history as the most determining factor in T2D prediction, followed by age, hypertension history, Uric acid, and TG. Conclusion: There was a significant association between hypertension and dyslipidemia history, TG, Uric acid, and hs-CRP with T2D development, along with age, WC, and blood pressure through the LR and DT methods.

Construction framework of smart tourism big data mining model driven by blockchain technology.

In the era of sharing economy, the tourism market is increasingly characterized by personalized demand, mobile consumption and product segmentation. This paper aims to apply big data mining technology in the field of smart tourism. Firstly, it focuses on image summary selection and collaborative filtering technology based on big data mining. It then demonstrates the integration of blockchain in smart tourism, emphasizing the use of decentralized structures and smart contracts to achieve data security and transparency, and describes the testing process of smart tourism platforms, including data preparation and platform operational efficiency testing. Finally, the research results of this paper are summarized, and the development potential and practical application value of smart tourism are demonstrated. The results show that in the smart tourism big data mining model, the minimum support for the data set is 10 % and 20 %, respectively. Moreover, with the increase of the number of nodes in the same data set, the running time decreases gradually. It can be seen that smart tourism big data mining has strong scalability.

Network pharmacology-based exploration of molecular mechanisms underlying therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure with spleen Qi deficiency syndrome.

BACKGROUND: Chronic heart failure is a complex clinical syndrome. The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure; however, the underlying molecular mechanism is still not clear. AIM: To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure. METHODS: The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target genes of chronic heart failure were searched in the Genecards database. The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drug-disease targets, which were then used to construct a key chemical component-target network using Cytoscape 3.7.2 software. The protein-protein interaction network was constructed using the String database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database. Finally, our previously published relevant articles were searched to verify the results obtained via network pharmacology. RESULTS: A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified, of which quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3, MAPK3, AKT1, JUN, MAPK1, TP53, TNF, HSP90AA1, p65, MAPK8, MAPK14, IL6, EGFR, EDN1, FOS, and other proteins. The pathways identified by KEGG enrichment analysis include pathways in cancer, IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, cAMP signaling pathway, NF-kappaB signaling pathway, AMPK signaling pathway, etc. Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α, IL-6, MAPK, cAMP, and AMPK pathways to affect the mitochondrial structure of myocardial cells, oxidative stress, and energy metabolism, thus achieving the therapeutic effects on chronic heart failure. CONCLUSION: The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes, oxidative stress, energy metabolism, and other processes. Future studies are warranted to investigate the role of the IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.

Comparison of Infection Risks Between Various Inhaled and Intranasal Corticosteroids: A Pharmacovigilance Analysis Based on the FAERS Database.

Purpose: This study conducted a pharmacovigilance analysis based on the FDA Adverse Event Reporting System (FAERS) database to compare the infection risk of inhaled or nasal Beclomethasone, Fluticasone, Budesonide, Ciclesonide, Mometasone, and Triamcinolone Acetonide. Methods: We used proportional imbalance analysis to evaluate the correlation between ICS /INCs and infection events. The data was extracted from the FAERS database from April 2015 to September 2023. Further analysis was conducted on the clinical characteristics, site of infection, and pathogenic bacteria of ICS and INCs infection adverse events (AEs). We used bubble charts to display their top 5 infection adverse events. Results: We analyzed 21,837 reports of infection AEs related to ICS and INCs, with an average age of 62.12 years. Among them, 61.14% of infection reports were related to females. One-third of infections reported to occur in the lower respiratory tract with Fluticasone, Budesonide, Ciclesonidec, and Mometasone; over 40% of infections reported by Triamcinolone Acetonide were eye infections; the rate of oral infections caused by Beclomethasone were 7.39%. The reported rates of fungal and viral infections caused by beclomethasone were 21.15% and 19.2%, respectively. The mycobacterial infections caused by Budesonide and Ciclesonidec account for 3.29% and 2.03%, respectively. Bubble plots showed that the ICS group had more fungal infections, oral infections, pneumonia, tracheitis, etc. The INCs group had more eye symptoms, rhinitis, sinusitis, nasopharyngitis, etc. Conclusion: Women who use ICS and INCs are more prone to infection events. Compared to Budesonide, Fluticasone seemed to have a higher risk of pneumonia and oral candidiasis. Mometasone might lead to more upper respiratory tract infections. The risk of oral infection was higher with Beclomethasone. Beclomethasone causes more fungal and viral infections, while Ciclesonide and Budesonide are more susceptible to mycobacterial infections.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer.

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

A real-world pharmacovigilance study of FDA adverse event reporting system events for atogepant.

BACKGROUND: Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine. METHODS: We collected data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Four algorithms (ROR, PRR, BCPNN, and EBGM) were used as measures to detect signals of atogepant-associated adverse events (AEs) in real-world data. RESULT: Of the 3,552,072 reports, 2876 expressly stated the use of atogepant. Women accounted for the majority of adverse events (AEs), with a notable age concentration of 45-65 years. The percentage of reported adverse events was the highest in the United States. Significant system organ categories (SOC) included nervous system disorders, gastrointestinal disorders, nervous system disorders, surgical and medical procedures, ear and labyrinth disorders. Notably, preferred terms (PTs) related to atogepant include migraine, constipation, nausea, vertigo, somnolence, decreased appetite, dizziness and fatigue. Unexpected adverse events such as abnormal dreams, self-injurious ideation, brain fog, tension headache, nightmare, brain neoplasm, feeling abnormal, euphoric mood, hyperacusis and post concussion syndrome were also identified. CONCLUSIONS: The present investigation has detected new and unexpected signals of atogepant-related adverse drug reactions (ADRs). In order to confirm these solve safety issues that were previously overlooked, more research is necessary.

Rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion based on data mining technology. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŠ€æœ¯æŽ¢æžé’ˆç¸æ²»ç–—è‚¿ç˜¤ç›¸å ³æ€§å¤±çœ çš„é€‰ç©´è§„å¾‹.

OBJECTIVES: To analyze the rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion by data mining technology. METHODS: The articles of cancer-related insomnia treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, WOS, Cochrane, and Embase databases, from the inception of each database to January 5, 2024. The prescription database of acupuncture and moxibustion for cancer-related insomnia was established. The descriptive analysis was conducted on the use frequency, meridian tropism and distribution of acupoints. Using SPSS Modeler 18.0 Apriori algorithm, the association rules of acupoint prescriptions were analyzed. With Cytoscape3.9.1 software used, the complex network diagram was plotted, and the cluster analysis of high-frequency acupoints was performed by SPSS26.0 software. RESULTS: Forty-one articles were included, and 67 prescriptions were extracted with 89 acupoints involved, and the total use frequency was 447 times. The top 4 acupoints of the high use frequency were Baihui (GV20), Sanyinjiao (SP6), Shenmen (HT7) and Shenting (GV24). The included meridians were the governor vessel, the spleen meridian, the bladder meridian, the conception vessel, the heart meridian and the stomach meridian. The selected acupoints were mostly distributed on the head, the neck and and the upper and lower limbs. The special acupoints of the high use frequency included the five-Shu points, the crossing points and yuan-primordial points. Regarding acupoint combination, GV24, SP6, HT7, and GV20 were highly correlated. The three effective clusters were categorized among the top 12 acupoints of the high use frequency. CONCLUSIONS: In treatment of cancer-related insomnia with acupuncture and moxibustion, the principle focuses on supporting the healthy qi, eliminating pathogens, regulating yin and yang, promoting the circulation of the governor vessel for regulating the spirit, and tranquilizing the mind. The core acupoint prescription may includes GV24, SP6, HT7 and GV20；combined with Zusanli (ST36) and Yintang (GV4+) to enhance the therapeutic effect.